Flipbooks

Flipbooks present code step-by-step and side-by-side with its output. The incremental code/result reveal may help you digest longer manipulation and analysis pipelines as well as plot builds. Long pipelines of code can produce attractive outputs. But they can feel like long streches of highway with no exit or entry ramps. Interacting with such code that has been made into a flipbook is different. The user can isolate and gain insight about short streches along the pipeline that are useful for her or his particular project – all the while engaged by the creativity brought by the project creator in pursuit of their own goal. Flipbooks enable us to isolate how individual functions work in context.

Example

Click once in the box the below to initiate the session; then use the right and left arrow to “flip” through the book to see how to make an animated “racing” bar chart of the population size of the most populous countries over time.

Do It Yourself

Would you like to make a flipbook? We try to make that easy for you with {flipbookr}. To get started have a look at A minimal Flipbook, the template for which will be available once you install the flipbooks package as follows:

devtools::install_github("EvaMaeRey/flipbookr")

You will also likely use xaringan, an rmarkdown presentation builder:

install.packages("xaringan")

Steps to building your first flipbook using RStudio

  1. install.packages(“xaringan”)
  2. devtools::install_github(“EvaMaeRey/flipbookr”)
  3. restart RStudio
  4. File -> New File -> RMarkdown -> From Template -> A Minimal Flipbook
  5. Use template or your own pipelines
  6. “Knit”
  7. Explore your flipbook!

Using the steps above, the template will create the following book (you can explore the different modalities here):

But we’ve all got issues

Here are a few known issues to watch for:

  1. You will need an internet connection to build Xaringan output, unless you use this set up:
    • within a project execute xaringan::xaringan::summon_remark() in your console
    • then in the YAML right after your css declaration, add, chakra: libs/remark-latest.min.js
  2. Comments in flipbooked pipelines should not contain parentheses
  3. Quoted text may not span lines (must be on a single line)
  4. Xaringan produces an html file and associated files. This means:
    • The output is not one, self-contained file
    • Output to be displayed in the flipbook must be supported in Xaringan and html

Flipbooks built in-house

Here at the University of Denver, we’ve built a few flipbooks. Here are some of them.

If you need a theoretical exploration of the grammar of graphics and its use with ggplot2, see The ggplot2 grammar guide.

The geom pile on is a suppliment to the ggplot grammar guide and shows you a lot of “geoms” that you can use to communicate about your data. Piling up the geoms allows you to see howing plot types relate to one another.

ggplot themes takes you through logic of the ggplot2 theme adjustments.

For examples of data wrangling/manipulation with pipelines and pipes (%>%), check out The Tidyverse in Action.

the data cleaning flipbook gives you some life-changing, magical, and real-life examples of reading data into R and cleaning it (aka “tidying up”). Demonstrates uses of {janitor}, {countrycode}, and {lubridate} as well as regex with {stringr}.

the ggplot flipbook walks through a variety of examples of plot built using ggplot2.

Galton Board shows you how to build your own Galton Board with R, ggplot2, and gganimate.

leaflet for R (interactive maps) walk through some interactive map builds using the {leaflet} package (load slow!).

the racing barchart will give you a chance to flex your ggplot2 and gganimate muscles.

D’Hondt/Jefferson Allocation helps to explain just how seats are allocated under this common political decision rule.

UNGA voting data is a flipbook that looks at the number of votes taken in the UN General Assembly. It also shows how you can make a lot of lateral moves and even backward steps with your decisions in ggplot2 - like with facet_null() and overwriting aesthetic decisions with NULL - aes(color = NULL).

a data.table flipbook is an introduction to the popular data manipulation package, {data.table}.

#TidyTuesday walk through showcases a few of the beautiful contributions to the #TidyTuesday initiative

[simpsona]

Stacked Column -> Pie Chart reveals some of the deep logic of the grammar of graphics — Stacked column plots, and pie plots and donut plots are related by choices in coordinate systems and extent of axes.

Implementation

If you are interested in contributing to {flipbookr}, you might consider getting some of the intuition of the internal functions by browsing through the resource below, or link to it here:

Origins

In mid 2018 Garrick Aden-Buie, a data scientist at the Moffitt Cancer Center, created and debuted a side-by-side format for code and figure output in the slide-show tool, Xaringan, an RMarkdown tool. He demonstrated in incremental steps how someone might build up a plot using the popular R visualization package ggplot2. In August 2018, I emulated this work, but using a code style I developed which I called “slowggplot2”, which is entirely sequential and more incremental than typical workflows. This style uses one ggplot function for every plotting decision that is made (when possible); this format is ideal for automatically parsing and reconstructing partial builds of the plots; reconstructions clearly connect the coding decision and associated behavior. In September, Emi Tanaka, a Lecturer in Statistics at the University of Sydney, implemented a similar sequential workflow demonstration, which she partially automated.

I approached Garrick and Emi in October about working together on “flipbooks”. We soon had a fully automated framework to parse ggplot2 code, and present the code reconstruction along with associated output!

The procedure to automatically parse and reconstruct code was tested and refined on small projects. Then, I created a “flipbook” of a sizable collection of my portfolio of data visualizations, originally created for the initiative #MakeoverMonday. I publicized the work in February, 2019; the ggplot flipbook was met an enthusiastic reception.

Since then, I’ve taken the development lead in making a number of changes to the foundational code to improve the tool and to extend use cases. Many of these improvements are discussed in the template.

  • Adding more “connection” operators as break points for code (%>%, ->, %+%) to enable presentation beyond data visualization (initially we only used the +)
  • Allowing code wrapping (breaks occur not necessarily at the end of lines)
  • Determining partial code completeness based on paretheses balancing
  • Using code parser rather than regular expressions to identify role of text segments
  • Allowing the user to define the reveal points (with the special #BREAK comment)
  • Allowing the user to define non-sequential reveal (with #BREAK2, #BREAK3 etc)
  • Allowing code to be multiply revealed (multiple realizations of same code)
  • Allowing for output only or code only display (as opposed to just side by side)
  • Tracking objects created with left assignment
  • Separating the parsing, code reconstruction, and presentation platform delivery steps, to make code more flexible, and robust.

Reception

The feedback on the flipbook format has been very encouraging (thank you if you were a part of this!) and a big motivation to keep working on this project. The ggplot flipbook was a top story on Hacker News and was featured as a highlight in the R Weekly newsletter.

Some of the most encouraging feedback about flipbooks has come from individuals, via Twitter. Their comments can be viewed here.

Feedback comes from individuals around the world including the following locations!

Houston, TX | Strijtem, Flanders (Belgium) | Rotterdam, The Netherlands | Utah, USA | Buenos Aires | Utah, USA | Chicago, IL | España | Washington, DC | España | Chicago | Baltimore, MD | South Africa | Aalborg, Denmark | Dresden, Germany | Orlando, FL | Philadelphia, PA | Oslo, Norway | Omaha, NE | Rotterdam, The Netherlands | UK | Switzerland | Turners Falls, MA | Montpellier, France | San Francisco, CA | San Francisco, CA | Hong Kong | Fairfax, Virginia USA | Bundoora,Victoria, Australia | Ile-de-France, France | Bundoora,Victoria, Australia | On Your Left || Bridge 4 | Chicago | Lansing, MI | Austin, TX | New York, NY | United States | United Kingdom | Chicago | Palmerston North, New Zealand | Coventry/Birmingham | Cambridge, MA | Strasbourg, France | Sydney, New South Wales | Dublin City, Ireland | RTP, NC | Tallahassee, Florida, USA | Princeton, NJ | Carrboro, NC | Yokohama City Tsurumi Ward | Rio | Boston | Sheffield/Leeds | moon | Singapore | London, England | Lisbon, Portugal | Ghana. | Vancouver, BC | Santa Barbara, CA | Houston, TX | Waco, Texas | Victoria, British Columbia | Austin, TX | Quincy, MA | Chicago, IL | Dallas, TX | Canada | Utrecht | Paris, France | Philadelphia, PA | Sydney, New South Wales | Toronto, Canada | Utrecht, The Netherlands | Ho Chi Minh, Viet Nam | Lima, Peru | Sydney | Sydney, Australia | roFL